Translational Metabolic Lab Using Omics to Translate Research to Diagnostics

Translational Metabolic Laboratory
Using Proteomics, Glycomics and Metabolomics to
translate Research to Biomarkers to Diagnostics
April 2015
Translational Metabolic Laboratory, Department of Laboratory Medicine
https://www.radboudumc.nl/Research/ProteomicsMetabolomicsGlycomics/

Radboudumc
• Mission: “To have a significant impact on healthcare”
• Strategic focus on Personalized Healthcare through “the
patient as partner”
• Core activities:
• Patient care
• Research
• Education
• 11.000 colleagues
• 50 departments
• 3.000 students
• 1.000 beds
• First academic centre outside US to fully implement EPIC

Patient
Radboud
Personalized Healthcare
A significant impact
on healthcare
Molecule
Population
3

Personalized Healthcare @ Radboudumc
People are different Stratification by multilevel diagnosis
+Patient’s preference of treatment
Exchange experiences in
care communities Select personalized therapy
Population
Patient
Molecule
4

www.radboudumc.nl/research/technologycenters
Genomics
Bioinformatics
Animal
studies
Stem
cells
Translational
neuroscience
Image-guided
treatment
Imaging
Microscopy
Biobank
Health
economics
Mass
Spectrometry
Radboudumc
Technology
Centers
Investigational
products
Clinical
trials
EHR-based
research
Statistics
Human
physiology
Data
stewardship
Molecule
Flow
cytometry
March 2015

Opening Radboud Research Facilities, 2nd Oct 2014
Point of contact: Alain van Gool
About 250 dedicated people working in 18 Technology Centers, ~1600 users (internal, external), ~140 consortia
www.radboudumc.nl/research/technologycenters/
6
Genomics
Bioinformatics
Animal
studies
Stem
cells
Translational
neuroscience
Image-guided
treatment
Imaging
Microscopy
Biobank
Health
economics
Mass
Spectrometry
Radboudumc
Technology
Centers
Investigational
products
Clinical
trials
EHR-based
research
Statistics
Human
physiology
Data
stewardship
Molecule
Flow
cytometry

7
About 250 dedicated people working in 18 Technology Centers, ~1600 users (internal, external), ~140 consortia
www.radboudumc.nl/research/technologycenters/
• Proteins
• Metabolites
• Drugs
• PK-PD
• Preclinical
• Clinical
• Behavioural
• Preclinical
• Animal facility
• Systematic review
• Cell analysis
• Sorting
• Pediatric
• Adult
• Phase 1, 2, 3, 4
• Vaccines
• Pharmaceutics
• Radio-isotopes
• Malaria parasites
• Management
• Analysis
• Sharing
• Cloud computing
• DNA
• RNA
• Internal
• External
• Early HTA
• Evidence-based
surgery
• Field lab
• Statistics
• Biological
• Structural
• Preclinical
• Clinical• Economic
viability
• Decision
analysis
• Experimental design
• Biostatistical advice
• Electronic Health Records
• Big Data
• Best practice
• In vivo
• Functional
diagnostics
• iPSC
• Organoids

Translational medicine @ Radboudumc

Research Biomarkers Diagnostics
Department of Laboratory Medicine, Radboudumc
Integrated Translational Research and Diagnostic Laboratory, 220 fte, yearly budget ~ 28M euro.
Close interaction with Dept of Genetics, Pathology and Medical Microbiology
Specialities:
• Proteomics, glycomics, metabolomics
• Enzymatic assays
• Neurochemistry
• Cellulair immunotherapy
• Immunomonitoring
Areas of disease:
• Metabolic diseases
• Mitochondrial diseases
• Lysosomal /glycosylation disorders
• Neuroscience
• Nefrology
• Iron metabolism
• Autoimmunity
• Immunodeficiency
• Transplantation
In development:
• ~500 Biomarkers
• Early and late stage
• Analytical development
• Clinical validation
Assay formats:
• Immunoassay
• Turbidicity assays
• Flow cytometry
• DNA sequencing
• Mass spectrometry
• Experimental human (-ized)
invitro and invivo models for
inflammation and
immunosuppression
Validated assays*:
• ~ 1000 assays
• 3.000.000 tests/year
Areas of application:
• Personalized healthcare
• Diagnosis
• Prognosis
• Mechanism of disease
• Mechanism of drug action
Department of Laboratory Medicine
*CCKL accreditation/RvA/EFI
www.laboratorymedicine.nl
9

One genome → multiple proteomes/metabolomes
• The proteomes and metabolomes are the functional output of
the genome
• 21.000 genes → approximately 500.000 possible proteins and
isoforms and biochemical metabolites
• Proteomes define and reflect the functional state of a cell or
organism at a certain time under certain conditions
• Proteomes and metabolome change depending on stimuli and
challenges; most cell/tissue signalling occurs through rapid
protein changes
• Proteomics and metabolomics are strong approaches to
identify and analyse metabolic changes of cell/tissue/organism
• Unique added value of proteomics:
• Protein expression
• Post-translational modifications
• Protein complex formation + function

One genome → multiple proteomes
Body fluids
Tissues
Cells
Plasma Urine CSF
Lung Colon Adrenal gland
THP-1 Jurkat Granulosa cells

Proteomics MetabolomicsGlycomics
Mass spectrometry – NMR based, 20 dedicated fte, part of diagnostic laboratory (Department of
Laboratory Medicine), close interaction with Radboudumc scientists and external partners
Translational Metabolic Laboratory – Laboratory Medicine
Ron Wevers, Jolein Gloerich, Alain van Gool, Leo Kluijtmans, Dirk Lefeber, Hans Wessels, et al

Mass spectrometry – NMR based, 20 dedicated fte, part of diagnostic laboratory (Department
Laboratory Medicine), close interaction with Radboudumc scientists and external partners
Key experts:
Proteomics
Jolein Gloerich
Hans Wessels
Alain van Gool
Glycomics
Monique Scherpenzeel
Dirk Lefeber
Metabolomics
Leo Kluijtmans
Ron Wevers

Research
• Projects
• Service
External
• Projects
• Service
Patient care
• Health care focus
• Biomarkers, diagnostics
• Consortia (NL, EU)
Key features:
• Expertise centre rather than service facility
• Focus to translate Research to Biomarkers to Diagnostics
• Application of many years Omics expertise to customer’s specific needs
• Ambition to grow with long-term strategic projects, collaborations, staff and impact

Radboud Proteomics Center
Bottom up
proteomics
Top down
proteomics
Targeted
proteomics
Peptide-based
Differential Protein Profiling
Relative Quantitation
Intact protein-based
Post Translational Modifications
Peptide-based
Selected biomarkers
Quantitative analysis

Proteomics techniques
• Peptide-based identification of proteins
• Differential protein expression profiling
(labelfree/labeled)
• Suitable for very complex samples
(in combination with fractionation)
• Focus on research
Whole proteome analysis
Protein complex isolation and characterization
Bottom up
Proteomics

Applications • Differential protein expression in:
• Health/disease
• Time
• Before/after treatment
• Protein-protein interactions:
• Protein complexes
• Protein correlation profiling
Up regulatedDown regulated
Instruments:
Bottom up
proteomics

Proteins Peptides Data Analysis
Phase1
RP pH2.7
LC-MS/MS
Trypsin
1D LC MS/MS workflow
CONTROLS
CONDITION 1
CONDITION 2
• Body fluids
• Circulating vesicles
• Tissues
• Cells
• Organelles
• Membranes
• Protein complexes
• Single proteins
Samples:
Bottom up
proteomics

Example cellular proteome profiling
Sample: HEK293 whole cell proteome (1 µg tryptic digest of urea extract)
1D LC-M/MS proteomics analysis
Retention time
m/z
400
600
800
1000
1200
1400
m/z
10 20 30 40 50 60 Time [min]
Blue: signal intensity in MS
Pink dots: precursors selected for MS/MS
Detected peaks in MS spectra 1.584.599
Detected isotope patterns in MS spectra 130.172
Total number of MS/MS spectra 22.743
Av. Absolute Mass Deviation [ppm] 2,8972
Matched MS/MS spectra 5.603
Identified NR peptides 4.537
Identified proteins 1.321
False Discovery Rate 0,98%
Bottom up
proteomics
In 1 scan:

Proteins Peptides RP pH10 UPLC 20 fractions
Phase1Phase2
20 fractions RP pH2.7
LC-MS/MS
Data processing
Statistical
analysis
400
600
800
1000
1200
m/z
20 30 40 50 Time [min]
Trypsin
CONTROLS
CONDITION 1
CONDITION 2
2D LC (RP x RP) MS/MS workflow
Bottom up
proteomics

719
94
109
41
246
845
1107
2D LC-MS/MS
60min gradients
1D LC-MS/MS
60min gradient
2D LC-MS/MS
20min gradients
963
1851
2765
0
500
1000
1500
2000
2500
3000
1DLC-MS/MS60min
2DLC-MS/MS20min
2DLC-MS/MS60min
1.9x
2.9x
Added value 2D LC-MS/MS
1D RP LC-MS/MS versus 2D RPxRP LC-MS/MS: HEK293 cell line
Bottom up
proteomics

Added value 2D LC-MS/MS
692
1769
0
200
400
600
800
1000
1200
1400
1600
1800
2000
1D LC-MS/MS 2D LC-MS/MSIdentifiedproteins
2.6x
1264
505
187
2D LC-MS/MS
60min gradients
1D LC-MS/MS
60min gradient
1D RP LC-MS/MS versus 2D RPxRP LC-MS/MS: Fat biopt sample
Bottom up
proteomics

Example tissue profiling project
Protein expression
(positive controls)
GO Protein distributions
Cellular compartments
LFQ scatter plot
Biological replicates
y= 0.9834x + 130390
R2=0.9842
Q: downstream effects of transgene?
Hippocampus tissue of Transgenic mice
4 Conditions: WT, TG, WT treated, TG treated with drug
5 Biological replicates; 2D LC-MS/MS analysis (20 fractions, 1 hour gradient)
Label-Free Quantitation (LFQ – MaxQuant)
• LC-MS/MS analyses: 400
• MS spectra: 1.937.394
• MS/MS spectra: 2.323.458
• Detected isotope patterns: 66.602.271
• Isotope patterns sequenced: 1.295.489
• Average absolute mass deviation: 1,38 ppm
• 1,3 Terrabyte data
PCA analysis – loading plot
Bottom up
proteomics
• Matched MS/MS spectra to peptides: 500.317
• Identified proteins: 3.187
• Quantified proteins: 2.365 (≥2 peptides/protein)
• Differential proteins: 276 (p<0.05)
• Average CV < 21%*
* Combining biological and technical reproducability
Transgene
Downstream

Proteins SDS-PAGE 9 Gel slices 9 in-gel digests
Phase1Phase2
9 Samples RP pH2.7
LC-MS/MS
Data processing
Statistical
analysis
400
600
800
1000
1200
m/z
20 30 40 50 Time [min]
Gel enhanced LC-MS/MS workflow
Trypsin
Bottom up
proteomics

Example of cellular proteome profiling project
Q: downstream miRNA effects on proteome?
A375 melanoma cell line
miRNA treated versus control
3 Biological replicates
GeLC-MS/MS analysis (5 slices, 1 hour LC gradients)
• Quantified proteins: 1.379 (≥2 peptides/protein)
• Differential proteins: 337 (p<0.05) / 151 (p<0.01)
• Good reproducibility (average CV < 20%)*
• Data analysis: 70% overlap LC-MS/MS and RNA-Seq data
* Combination biological and technical reproducability
PCA loading plot
Chromatogram and ion map of a gel fraction
Collaboration with Radboudumc, InteRNA, TNO (DTL hotel project)
Already
suspected outlier

Conclusions
Example of cellular proteome profiling project
Results
Samples
Up
regulated
Down
regulated
Differential analysis
-10
-5
0
5
10 ∞
∞
178 Differentially
expressed proteins
Results
Gene ontology: cellular localization
• 3,824 identified proteins (98.7% cell specific)
• 2,550 quantified proteins (≥ 2 peptides/protein)
• 178 differential proteins due to treatment:
• 138 proteins upregulated
• 40 proteins downregulated
• Good basis for follow-up pharmaco-proteomics
Q: how does proteome cell
line x look like?
Q: First look at effect
treatment on proteome
(feasibility)
→ GeLC-MS/MS approach
Bottom up
proteomics

Cluster: 28S mt-Ribosome
Cluster: 39S mt-Ribosome
Cluster: F1F0 ATP synthase
Cluster: cytochrome b-c1 complex
Cluster: NADH dehydrogenase & TCP1
Cluster: trifunctional enzyme & isocitrate dehydrogenase
Cluster: cytochrome C oxidase & mt-Ribosomal subcomplex
Example of complexome analysis project
Bottom up
proteomics
Collaboration with NCMD, Bob Lightowlers
Q: What subcomplexes in mitochondrial proteome?
HEK293 Mitochondrial fraction
2 BN gel lanes (4-12% AA & 5-15% AA)
24 gel slices per gel lane
• Migration profiles for 953 proteins
• Unambiguous ID of 24 known complexes
• Validation of 8 implied interactors of the mt-Ribosome
• 11 novel putative interactors of the mt-Ribosome
Hierarchical clustering

Fit-for-purpose sample preparation
MARS-14 depletion
GeLC-MS/MS1D LC-MS/MS 2D LC-MS/MS GeLC-MS/MS1D LC-MS/MS 2D LC-MS/MS
A B C D E F
Human CSF
Bottom up
proteomics

Q: Changes in exosome proteome related to clinical phenotype?
Samples: - urine exosomes from patients with rejection after renal transplantation
- 4 subject groups (CTRL, REJ, CMV, BK)
Approach: - Gel enhanced 1D LC-MS/MS analysis (9 fractions)
- Per subject group: 2 different pools of multiple patients
- 2 separate experiments (LTQ FT Ultra & MaXis 4G)
Results: - Robust sample preparation is crucial
- In total 521 proteins identified
- Exosome enrichment confirmed by gene ontology classification (Cellular Components)
Collaboration with Department of Urology
Example of urine exosome analysis project
Bottom up
proteomics

Anammox batch reactor
PCA analysis – loading plot
2D Hierarchical
Clustering
Q: optimal growth conditions?
Anammox bacterium
3 Different growth conditions
4 Technical replicates
1D LC-MS/MS analysis (1 hour gradient)
• Identified proteins: 270
• Differential proteins: 75
• Excellent reproducibility (average CV < 6.5%)
LFQ scatter plot
technical replicates
y= 1.0141x + 1250.7
R2=0.9991
Example of biotechnology project
Bottom up
proteomics
Collaboration with Boran Kartal/Mike Jetten (FNWI RU)

Q: Effect of two bacterial growth conditions?
Desulfobacillus bacterium
2 Different growth conditions; 2 Biological replicates
GeLC-MS/MS analysis (9 slices, 1 hour gradient)
• Quantified proteins: 950
• Differential proteins: 245 (p<0.05) / 109 (p<0.01)
• Excellent reproducibility (average CV < 10%)*
* Biological replicates: technical reproducability likely better
Protein expression example
Example of biotechnology project
LFQ scatter plot
Biological replicates
y= 1.0167x -
49244
R2=0.998
PCA loading plot
PC1 (72.9%)
PC2(14.7%)
Collaboration with external client
Bottom up
proteomics

• Intact protein analysis
• Post-translational modification
• Analysis of low to medium
complexity samples
Top down
proteomics
LC-MS Ion map of protein complex with MS spectrum of one subunit
Deconvoluted protein spectrum
Instruments:

Applications
• Characterization of intact
proteins:
• Post-translational
modifications
• Protein processing
• Splice variants
• Protein complex analysis
• Composition
• Complex-specific subunit
variants
• Quality control of biotech
products
Top down
proteomics
Quantitative analysis of intact protein isoforms
Collaboration with Floris van Delft (Synnafix)

Complexes Native
Electrophoresis
60 Gel slices 60 in-gel digests
Phase1Phase2
60 Samples RP pH2.7
LC-MS/MS
Data processing
Complexome
Profile
400
600
800
1000
1200
m/z
20 30 40 50 Time [min]
Bottom-up Complexome Profiling workflow
Trypsin

Complexes Native
Electrophoresis
Gel slice of
interest
Protein extraction,
reduction and SPE
Phase1Phase2
Protein
sample
LC-MS/MS with
fraction collection
Data processing
Top-Down
profiling
Top-Down Complexome Profiling workflow
Survey View
500
1000
1500
2000
2500
m/z
10 20 30 40 50 60 70 Time [min]
15
24
23
11 128 10 16 17 26
18
209
19
222114
13
12 13 14 15 16 17 18 19 20 Time[min]
0.0
0.5
1.0
1.5
2.0
2.5
7x10
Intens.

Phase3 Integrated Complexome Profiling workflow
Protein fractions
of interest
Peptides RP pH2.7
LC-MS/MS
Peptide MS2
level Data
nESI-MS/MS
Protein MS2
level data
Characterized
proteoform
Trypsin

Example of complexome analysis
Survey View
500
1000
1500
2000
2500
m/z
10 20 30 40 50 60 70 Time [min]
'1009.7168
10+
'1121.7954
9+
'1261.8938
8+
'1442.0208
7+ '1682.1905
6+
'2018.4295
5+
+MS, 56.8-58.7min #3408-3522
0
1
2
3
4
5
4x10
Intens.
1000 1200 1400 1600 1800 2000 2200 m/z
5+
6+
7+
8+
9+
10+
5+
6+7+
8+
9+
10+
1.682 m/z Da
Q: Composition of mitochondrial
complex 1?
• Y. lipolytica complex 1 as a model
for human
• 42 established subunits (7 mtDNA,
35 nDNA)
• Unknown mature subunit forms
• Unknown and dynamic post-
translational modifications
• Study: Combine Top-Down and
Bottom-Up characterization of all
subunits
Collaboration with Ulrich Brandt
Top down
proteomics

LC-MS ion map of 40-subunit protein complex
Survey View
500
1000
1500
2000
2500
m/z
10 20 30 40 50 60 70 Time [min]

ESI spectrum of 1 subunit
Survey View
500
1000
1500
2000
2500
m/z
10 20 30 40 50 60 70 Time [min]
'1009.7168
10+
'1121.7954
9+
'1261.8938
8+
'1442.0208
7+ '1682.1905
6+
'2018.4295
5+
+MS, 56.8-58.7min #3408-3522
0
1
2
3
4
5
4x10
Intens.
1000 1200 1400 1600 1800 2000 2200 m/z
5+
6+
7+
8+
9+
10+
5+
6+7+
8+
9+
10+
1.682 m/z Da

Top down / bottom up analysis of subunit protein (13,2 kDa)
Top-Down LC-MS/MS (ETD)
Top-Down NSI-MS/MS (ETD)
Bottom-Up LC-MS/MS (CID & ETD)
Matched peptide sequences in red, amino acids matched as ETD fragment ions are marked yellow (only for Top-Down data)
Hypothesized protein form
• N-terminus processing: Targeting sequence cleavage at S18
• C-terminus processing: None
• Additional PTMs: None
Top down
proteomics

Overlay deconvoluted experimental and simulated spectra
'10923.3198
Mr
'10947.2792
Mr
'10961.2630
Mr
CI filtered Captive 3ul 05FA_Tray02-E1_01_1071.d: +MS, 11.2-12.1min, Deconvoluted (MaxEnt, 503.10-2187.28, *0.063125, 50000)
CIfilteredCaptive₃ul₀₅FA_Tray₀₂-E₁₀1₁071.d:C₄₈₀H₇₄₃N₁₃₉O₁₅₂S₄, , 11014.3560
CIfilteredCaptive₃ul₀₅FA_Tray₀₂-E₁₀1₁071.d:C₄₇₇H₇₃₄N₁₃₈O₁₅₂S₃, , 10923.3105
0.0
0.2
0.4
0.6
0.8
1.0
6x10
Intens.
0.0
0.2
0.4
0.6
0.8
1.0
1.2
6x10
0.0
0.2
0.4
0.6
0.8
1.0
1.2
6x10
10920 10940 10960 10980 11000 11020 m/z
Measured spectrum
Simulated spectrum - unprocessed form
(database entry)
Simulated spectrum - hypothesized form
(according to MS/MS results)
Top down
proteomics

Characterization of complex subunits
Q: Composition of mitochondrial complex 1?
•Predicted: 42 subunits (7 mtDNA, 35 nDNA)
•Detected: 240 protein subunit isoforms
(truncations, PTMs)
•Straight but time-consuming path to subunit
characterization
Top down
proteomics

Intact complexome analysis from tissue biopsies
Pilot study:
• Native tissue biopsies
• Isolate membrane complexes
• Separate and isolate complexes using Blue Native gels
• LC-MS/MS analysis
• Data analysis
Tissue 1
(n=3)
Tissue 2
(n=3)
Subunit
Subunit – tissue 1
Subunit – tissue 2
• Identified protein sequence of subunit
• Deduce simulated sequences from database
• Determine fit with experimental data
Top down
proteomics

Example of diagnostic top-down proteomics
• 12 families with liver disease and dilated cardiomyopathy (5-20 years)
• Initial clinical assessment didn’t yield clear cause of symptoms
• Specific sugar loss of serum transferrin identified via glycoproteomics
ChipCube-LC- Q-tof MS
• Outcome 1: Explanation of disease
• Outcome 2: Dietary intervention as succesful personalized therapy
• Outcome 3: Glycoprofile transferrin developed and applied as diagnostic test
• Genetic defect in glycosylation enzyme (PGM1) identified via exome sequencing
{Tegtmeyer et al, NEJM 370;6: 533 (2014)}
Genomics Glycomics Metabolomics
Top down
proteomics
By Monique van Scherpenzeel, Dirk Lefeber

Radboud Proteomics Center
Bottom up
proteomics
Top down
proteomics
Targeted
proteomics
Peptide-based
Differential Protein Profiling
Relative Quantitation
Intact protein-based
Post Translational Modifications
Peptide-based
Selected biomarkers
Quantitative analysis

• Peptide-based
• Sensitive quantitative analysis
• Suitable for very complex
samples
Targeted
proteomics
Nature Methods:
Method of the year 2012
protein expression data
Data Analysis
Protein A isoform 1
Protein A isoform 2
Protein B

Applications
(Absolute) quantitation of protein biomarkers:
• Biomarker research: Quantitative analysis of specific set of proteins
• Biomarker validation: Validation and prioritization of selected biomarkers
• Diagnostics: Analysis of qualified biomarkers
Targeted
proteomics
Research Diagnostics
Instruments:

Biomarker innovation gap
• Imbalance between biomarker discovery, validation and application
• Many more biomarkers discovered than available as diagnostic test
50

Selection of
biomarkers
Single Reaction Monitoring workflow
Phase1
Selection of
optimal
peptides
• Unique
• Best detectable
in LC-MS
Optimize detection by
selecting optimal transitions
Phase2
Proteins Peptides Data AnalysisRP pH2.7
LC-MS/MS
Trypsin
Isotope
labeled
standards
Isotope
labeled
standards
Targeted
proteomics

LCM-proteomics workflow
Laser Captue
Microdissection
Samplepreparation
Proteins
Trypsin
Peptides
9 µm tissue
sections
LC-MS/MS
Data AnalysisTargeted SRMData Analysis
CONTROLS
CONDITION 1
CONDITION 2
1D LC-MS/MS
Biomarker Discovery Biomarker Validation
Targeted
proteomics

• Proteomics
• Bottom-up (shot-gun) proteomics
• Targeted proteomics
• Top-down proteomics
• Glycomics
• Glycan profiling
• (Targeted) Glycoproteomics
• Metabolomics
• Untargeted metabolomics
• Targeted metabolite profiling
Key experts:
Jolein Gloerich
Hans Wessels
Alain van Gool
Monique Scherpenzeel
Dirk Lefeber
Leo Kluijtmans
Ron Wevers

Source: Allison Doerr, Nature Methods 9,36 (2012)
Glycomics

Glycosylation markers in human medicin
• Biomarker for disease and therapy monitoring: rheumatoid arthritis,
oncology, hepatitis
• MUC2 glycosylation in colon carinoma
• Human blood groups (A, B, O, AB)
• CDTect (Carbohydrate-Deficient transferrin)
• Infectious diseases
• IgA nephropathy
1% of genes directly involved in glycosylation
About 50% of proteins is glycosylated
IgA

Glycosylation types
• N-glycosylation
• Asparagin linked
• 8 - 20 saccharides
• O-glycosylation
• Serine/Threonine linked
• <10 sacchariden
• Glycosaminoglycans
• 100-200 disaccharide units
• Agrin, Perlecan, Syndecan, Glypican
• Glycolipids

Diagnostics Research
Urinary glycan
profiling
Serum glycan
profiling
O-glycan profiling
PNGaseF chip
Chemical biology
Glycopeptide
profiling
glycolipid
profiling
Whole protein
glycoprofiling
Nucleotide-
sugars
Glycomics approaches

Glycomics application areas
• Mechanisms of glycosylation disorders
Linking genes to glycomics profiles
Understanding neuromuscular pathophysiology
• Glycomics Technology Platform
Services
Functional foods
Glycan tracers
Biomarkers

Glycomics
Intact
glycoprotein
Free glycans
Glycopeptides
500
750
1000
1250
1500
1750
m/z
10 15 20 25 30 35 40 Time [min]
PGM1 profile
CID fragmentation spectrum

Example: Intact glycoprotein biomarker
• 12 families with liver disease and dilated cardiomyopathy (5-20 years)
• Initial clinical assessment didn’t yield clear cause of symptoms
• Specific sugar loss of serum transferrin identified via glycoproteomics
ChipCube-LC- Q-tof MS
• Outcome 1: Explanation of disease
• Outcome 2: Dietary intervention as succesful personalized therapy
• Outcome 3: Glycoprofile transferrin developed and applied as diagnostic test
• Genetic defect in glycosylation enzyme (PGM1) identified via exome sequencing
{Tegtmeyer et al, NEJM 370;6: 533 (2014)}
Genomics Glycomics Metabolomics
60

Example: Glycopeptide profiling
• Optimized procedure using simple sample prep of plasma
• Detection of ~12.000 unique deconvoluted monoisotopic masses per
single analysis (> 50% are glycopeptides)
500
1000
1500
2000
m/z
5 10 15 20 25 30 35 40 Time [min]
Proof of principle study:
Monique van Scherpenzeel, Dirk Lefeber, Hans Wessels, Alain van Gool
Translational Metabolic Laboratory, Radboudumc, unpublished data

Example: Glycan analysis by nanoChip-QTOF MS
• High-resolution glycoprofiling
• Microfluidic chip system results in simplified operating conditions, increased
reproducibility and robustness
• CHIP formats: C18, Carbograph, C8, HILIC, phosphopeptides, PNGaseF

Bio-informatics :
• Coupling with public glyco-databases
• Annotation of glycan linkages
Glycan profiling in serum
B4GalT1

Metabolomics approaches
Diagnostics
• Organic acids
• Amino acids
• Purines&Pyrimidines
• Monosaccharides/Polyols
• Carnitine(-esters)
• Sterols
Research
• Assay development for specific
metabolites or metabolite classes
• Untargeted metabolite profiling
• Metabolite biomarker identification
Equipment
• GC
• 2 GC-MS
• 3 LC-MS/MS
• 2 amino acid analysers
• HPLC

Example: targeted diagnostics in metabolic disease
Amino acids
Amino acid analyser
Carnitine-ester profile
LC-MS/MS
Purines & pyrimidines
- HPLC & LC-MS/MS
Organic acids
GC-MS

DIAGNOSIS OF INBORN ERROR OF METABOLISM
Example: untargeted metabolomics to diagnose
individual patients
Human plasma
20 controls vs 1 patient
Agilent QTOF MS-data
- Reverse phase liquid chromatography
- Positive mode
- Features
•Accurate mass (165.07898)
• Retention time
• Intensity
XCMS
Alignment
Peak comparison
> 10000 Features
Chemometric pipeline
• T-test
• PCA
• P95
Metabolite identification
Online database HMDB
phenylalanine

A blind study
Plasma sample choice : Dr. C.D.G Huigen
Analytical chemistry : E. van der Heeft
Chemometrics : Dr. U.F.H. Engelke
Diagnosis : Prof. dr. R.A. Wevers;
Dr. L.A.J. Kluijtmans
 Test 10 samples from 10 patients with 5 different
Inborn Error of Metabolism’s
 21 controls

The blind study
 MSUD (2) → leucine, isoleucine, valine, 3-methyl-2-oxovaleric acid
 Aminoacylase I deficiency (2) → N-acetylglutamine, N-acetylglutamic acid,
N-acetylalanine, N-acetylserine, N-acetylasparagine, N-acetylglycine
 Prolinemia type II (2) → proline, 1-pyrroline-5-carboxylic acid
 Hyperlysinemia (2) → pipecolic acid, lysine, homoarginine, homocitrulline
 3-Hydroxy-3-methylglutaryl-CoA lyase deficiency (2) → 3-methylglutaryl-carnitine, 3-
methylglutaconic acid, 3-hydroxy-2-methylbutanoic acid, 3-hydroxy-3-methylglutaric acid
Diagnostic metabolites found in blood plasma
• Correct diagnosis in all 10 patients
• Five different IEM’s identified by
differential metabolites
• The approach works!!!
• Validated method  diagnostic SOP
• Planned for execution in line with genetics

2012
Patient
Targeted
Metabolic
screen
Targeted
gene
analysis
Diagnosis
+ follow-up
2013 / 2014
Patient
Whole
exome
sequencing
Targeted
confirmatory
metabolite +
enzyme
testing
Diagnosis
+ follow-up
Targeted assays vs holistic approach
Next
generation
metabolic
screening
Times are changing… (functional) genome analysis

Human
samples
Plasma, CSF (urine)
Controls vs. patient
QTOF Mass Spectrometry
- Reverse phase liquid chromatography
- Positive and negative mode
- Features
XCMS
Alignment
Peak comparison
> 10,000 Features
Personalized metabolic diagnostics
Xanthine Uric acid
72
Full metabolite profile:
Highly suspected of
xanthinuria

A problem in biomarker land
Imbalance between biomarker discovery and application.
• Gap 1: Strong focus on discovery of new biomarkers, few biomarkers progress
beyond initial publication to multi-center clinical validation.
• Gap 2: Insufficient demonstrated added value of new clinical biomarker and
limited development of a commercially viable diagnostic biomarker test.
Discovery Clinical
validation/confirmation
Diagnostic
test
Number of
biomarkers
Gap 1
Gap 2
74
The innovation gap in biomarker
research & development

Some numbers
Data obtained from Thomson Reuters Integrity Biomarker Module
Eg Biomarkers in time: Prostate cancer
May 2011: 2,231 biomarkers
Nov 2012: 6,562 biomarkers
Oct 2013: 8,358 biomarkers
25 Sep 2014: 9,975 biomarkers with
31,403 biomarker uses
EU: CE marking
USA: LDT, 510(k), PMA

Shared biomarker research through open innovation
We need to set up a open innovation network to share biomarker knowledge and
jointly develop and validate biomarkers (at level of NL and EU):
1. Assay development of (diagnostic) biomarkers
2. Clinical biomarker quantification/validation/confirmation
Shared knowledge,
technologies and objectives
Funding: NL – STW; EU - Horizon2020, IMI; Fast track pharma funds

Good example of multi-center biomarker validation

Biomarker Development Center (Netherlands)
STW perspectief grant
Biomarker Development Center
Public-private partnership 4 years
Project grant 4.3M Eur of which 2.2M government,
and 2.1M industry (0.9M cash/1.2M kind)
Close interactions with:
- Clinicians (biomarker application)
- Industry
- Patient stakeholder associations
Open
Innovation
Network !

Biomarker Development Center (Netherlands)
79

healthy disease disease +
treatment
Challenge: how to identify subpopulations in
Personalized Healthcare?
healthy disease disease +
treatment
• Biomarkers in populations often have a wide range
• Within this range, subpopulations can behave quite differently
• Chemometric methods dealing with multiple biomarker data points are needed
to reveal such individual differences and enable personalized medicine
(Source: Jasper Engel, Lionel Blanchet, Udo Engelke, Ron Wevers and Lutgarde Buydens)
80

Approach
Multiple
biomarker
datapoints
Chemometrics
Kernel transformation
Biosamples
Apply methods to identify subpopulations for Personalized Medicine
Urine NMR
81
(Source: Jasper Engel, Lionel Blanchet, Udo Engelke, Ron Wevers and Lutgarde Buydens)

Contact information
• Proteomics
• Glycomics
• Metabolomics
• Biomarkers
Visiting address: Radboud umc, route 774/830
https://www.radboudumc.nl/Research/ProteomicsMetabolomicsGlycomics/
http://laboratorymedicine.nl/104_theme_104_Translational-Metabolic-Laboratory.html
RadboudProteomicsCentre@umcn.nl
Jolein.Gloerich@radboudumc.nl
Alain.van Gool@radboudumc.nl
Monique.vanscherpenzeel@radboudumc.nl
Dirk.Lefeber@radboudumc.nl
Leo.Kluijtmans@radboudumc.nl
Ron.Wevers@radboudumc.nl
Alain.van Gool@radboudumc.nl
Ron.Wevers@radboudumc.nl

Translational Metabolic Lab Using Omics to Translate Research to Diagnostics

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